A recent study has leveraged machine learning techniques to identify the health and lifestyle factors that most significantly impact cognitive performance throughout life. Conducted on a diverse group of 374 adults between the ages of 19 and 82, the research pinpointed age, diastolic blood pressure, and body mass index (BMI) as the primary predictors of success on a focus-and-speed based attention test.
While diet and exercise were found to have a smaller but positive influence on cognitive outcomes, they did help mitigate the effects of higher BMI and blood pressure. This study underscores the importance of an integrated analysis of multiple health factors in understanding brain health as one ages.
Key findings from the study indicate that age, diastolic blood pressure, and BMI are crucial determinants of cognitive performance. Diet and physical activity contributed moderately to the ability to concentrate and respond quickly.
“This study used machine learning to evaluate a host of variables at once to help identify those that align most closely with cognitive performance,” said Naiman Khan, a professor of health and kinesiology at the University of Illinois Urbana-Champaign and lead researcher alongside kinesiology Ph.D. student Shreya Verma. “Standard statistical approaches cannot embrace this level of complexity all at once.”
The team gathered data encompassing participant demographics, including age, BMI, blood pressure, physical activity levels, and dietary patterns. They then analyzed the results from a flanker test designed to assess processing speed and cognitive control, where subjects needed to focus on a central arrow while ignoring misleading surrounding arrows.
Khan noted that earlier studies have linked adherence to healthful dietary measures — like the healthy eating index — to enhanced cognitive functions in older adults. He highlighted dietary patterns rich in antioxidants, omega-3 fatty acids, and vitamins for their association with improved cognitive performance. Diets such as the Dietary Approaches to Stop Hypertension (DASH), the Mediterranean diet, and the MIND diet have also been tied to protective benefits against neurodegenerative issues.
“Clearly, cognitive health is driven by a host of factors, but which ones are most important?” Verma questioned. “We wanted to evaluate the relative strength of each of these factors in combination with all the others.”
The research utilized various machine learning algorithms to determine which best assessed the different factors impacting the speed of accurate responses in the flanker test. While age was identified as the strongest predictor, followed by diastolic blood pressure and BMI, adherence to a healthy diet also correlated with favorable test performances.
“Physical activity emerged as a moderate predictor of reaction time, suggesting it may interact with other lifestyle factors such as diet and body weight to influence cognitive performance,” Khan remarked.
The findings indicate that employing machine learning can enhance the precision of research in nutritional neuroscience, offering an opportunity to tailor strategies for older populations, individuals facing metabolic risks, or anyone aiming to improve cognitive function through lifestyle interventions.
This research was supported by the Personalized Nutrition Initiative and the National Center for Supercomputing Applications at the University of Illinois. Khan is also affiliated with the Division of Nutritional Sciences, the Neuroscience Program, and the Beckman Institute for Advanced Science and Technology at Illinois.
For more detailed insights, the full study is published in The Journal of Nutrition.